Applicability domain based on ensemble learning in classification and regression analyses

Journal of Chemical Information and Modeling

Volumn 54, Issue 9, 2014, Pages 2469-2482

  Kaneko, Hiromasa ^a   Funatsu, Kimito ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

NUMERICAL METHODS;

CLASSIFICATION ANALYSIS; DATA DENSITY; ENSEMBLE LEARNING; STRUCTURAL RELATIONSHIP;

REGRESSION ANALYSIS;

LEARNING; REGRESSION ANALYSIS; THEORETICAL MODEL;

LEARNING; MODELS, THEORETICAL; REGRESSION ANALYSIS;

EID: 84916598358     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci500364e     Document Type: Article

References (36)

1. Gasteiger, J.; Engel, T. Chemoinformatics-A Textbook; Wiley-VCH: Weinheim, Germany, 2003.
2. Ajmani, S.; Jadhav, K.; Kulkarni, S. A. Three-dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation. J. Chem. Inf. Model. 2006, 46, 24-31.
3. Palmer, D. S.; O'Boyle, N. M.; Glen, R. C.; Mitchell, J. B. O. Random Forest Models to Predict Aqueous Solubility. J. Chem. Inf. Model. 2007, 47, 150-158.
4. Vapnik, V. N. The Nature of Statistical Learning Theory; Springer: New York, 1999.
5. Wold, S.; Sjöström, M.; Eriksson, L. PLS-Regression: A Basic Tool of Chemometrics. Chemom. Intell. Lab. Syst. 2001, 58, 109-130.
6. Bishop, C. M. Pattern Recognition and Machine Learning; Springer: New York, 2006.
7. Shen, Q.; Jiang, J. H.; Tao, J. C.; Shen, G. L.; Yu, R. Q. Modified Ant Colony Optimization Algorithm for Variable Selection in QSAR Modeling: QSAR Studies of Cyclooxygenase Inhibitors. J. Chem. Inf. Model. 2005, 45, 1024-1029.
8. Tetko, I. V.; Sushko, I.; Pandey, A. K.; Zhu, H.; Tropsha, A.; Papa, E.; Oberg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection. J. Chem. Inf. Model. 2008, 48, 1733-1746.
9. Lepp, Z.; Huang, C. F.; Okada, T. Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity. J. Chem. Inf. Model. 2009, 49, 2429-2443.
10. Igne, B.; Hurburgh, C. R. Local Chemometrics for Samples and Variables: Optimizing Calibration and Standardization Processes. J. Chemom. 2010, 24, 75-86.
11. Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Model. 2005, 45, 786-799.
12. Novotarskyi, S.; Sushko, I.; Korner, R.; Pandey, A. K.; Tetko, I. V. A Comparison of Different QSAR Approaches to Modeling CYP450 1A2 Inhibition. J. Chem. Inf. Model. 2009, 49, 2429-2443.
13. Horvath, D.; Marcou, G.; Varnek, A. Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of QSAR Models. J. Chem. Inf. Model. 2009, 49, 1762-1776.
14. Kaneko, H.; Arakawa, M.; Funatsu, K. Applicability Domains and Accuracy of Prediction of Soft Sensor Models. AIChE J. 2011, 57, 1506-1513.
15. Kaneko, H.; Arakawa, M.; Funatsu, K. Novel Soft Sensor Method for Detecting Completion of Transition in Industrial Polymer Processes. Comput. Chem. Eng. 2011, 35, 1135-1142.
16. Dimitrov, S.; Dimitrova, G.; Pavlov, T.; Dimitrova, N.; Patlewicz, G.; Niemela, J.; Mekenyan, O. A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models. J. Chem. Inf. Model. 2005, 45, 839-849.
17. Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A. K.; Cherkasov, A.; Lo, J. Z.; Gramatica, P.; Hansen, K.; Schroeter, T.; Müller, K.-R.; Xi, L. L.; Liu, H. X.; Yao, X. J.; Oberg, T.; Hormozdiari, F.; Dao, P. H.; Sahinalp, C.; Todeschini, R.; Polishchuk, P.; Artemenko, A.; Kuz'min, V.; Martin, T. M.; Young, D. M.; Fourches, D.; Muratov, E.; Tropsha, A.; Baskin, I.; Horvath, D.; Marcou, G.; Muller, C.; Varnek, A.; Prokopenko, V. V.; Tetko, I. V. Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set. J. Chem. Inf. Model. 2010, 50, 2094-2111.
18. Baskin, I. I.; Kireeva, N.; Varnek, A. The One-Class Classification Approach to Data Description and to Models Applicability Domain. Mol. Inf. 2010, 29, 581-587.
19. Kaneko, H.; Funatsu, K. Estimation of Predictive Accuracy of Soft Sensor Models Based on Data Density. Chemom. Intell. Lab. Syst. 2001, 58, 109-130.
20. Kaneko, H.; Funatsu, K. A Soft Sensor Method Based on Values Predicted from Multiple Intervals of Time Difference for Improvement and Estimation of Prediction Accuracy. Chemom. Intell. Lab. Syst. 2011, 109, 197-206.
21. Sheridan, R. P. Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest. J. Chem. Inf. Model. 2012, 52, 814-823.
22. Sheridan, R. P. Using Random Forest To Model the Domain Applicability of Another Random Forest Model. J. Chem. Inf. Model. 2013, 53, 2837-2850.
23. Kaneko, H.; Funatsu, K. Adaptive Soft Sensor Based on Online Support Vector Regression and Bayesian Ensemble Learning for Various States in Chemical Plants. Chemom. Intell. Lab. Syst. 2014, 137, 57-66.
24. Hou, T. J.; Xia, K.; Zhang, W.; Xu, X. J. ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach. J. Chem. Inf. Comput. Sci. 2004, 44, 266-275.
25. Baurin, N.; Baker, R.; Richardson, C.; Chen, I.; Foloppe, N.; Potter, A.; Jordan, A.; Roughley, S.; Parratt, M.; Greaney, P.; Morley, D.; Hubbard, R. E. Drug-like Annotation and Duplicate Analysis of a 23-Supplier Chemical Database Totalling 2.7 Million Compounds. J. Chem. Inf. Comput. Sci. 2004, 44, 643-651.
26. Sun, H. A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption. J. Chem. Inf. Comput. Sci. 2004, 44, 748-757.
27. Wegner, J. K.; Fröhlich, H.; Zell, A. Feature Selection for Descriptor Based Classification Models. 1. Theory and GA-SEC Algorithm. J. Chem. Inf. Comput. Sci. 2004, 44, 921-930.
28. Bergström, C. A. S.; Wassvik, C. M.; Norinder, U.; Luthman, K.; Artursson, P. Global and Local Computational Models for Aqueous Solubility Prediction of Drug-like Molecules. J. Chem. Inf. Comput. Sci. 2004, 44, 1477-1488.
29. Clark, M. Generalized Fragment-Substructure Based Property Prediction Method. J. Chem. Inf. Model. 2005, 45, 30-38.
30. Vidal, D.; Thormann, M.; Pons, M. LINGO, an Efficient Holographic Text Based Method To Calculate Biophysical Properties and Intermolecular Similarities. J. Chem. Inf. Model. 2005, 45, 386-393.
31. Kaneko, H.; Funatsu, K. Development of a New Regression Analysis Method Using Independent Component Analysis. J. Chem. Inf. Model. 2008, 48, 534-541.
32. http://www.talete.mi.it/products/dragon-description.htm (accessed March 31, 2014).
33. Wold, S. Principal Component Analysis. Chemom. Intell. Lab. Syst. 1987, 2, 37-52.
34. http://www.cadaster.eu/node/65 (accessed July 17, 2014).
35. Owen, J. R.; Nabney, I. T.; Medina-Franco, J. L.; López-Vallejo, F. Visualization of Molecular Fingerprints. J. Chem. Inf. Model. 2011, 51, 1552-1563.
36. http://www.chemaxon.com/ (accessed July 17, 2014).